Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another.Recently,Transformer-based neural machine translation(NMT)has a...Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another.Recently,Transformer-based neural machine translation(NMT)has achieved great break-throughs and has become a new mainstream method in both methodology and applications.In this article,we conduct an overview of Transformer-based NMT and its extension to other tasks.Specifically,we first introduce the framework of Transformer,discuss the main challenges in NMT and list the representative methods for each challenge.Then,the public resources and toolkits in NMT are listed.Meanwhile,the extensions of Transformer in other tasks,including the other natural language processing tasks,computer vision tasks,audio tasks and multi-modal tasks,are briefly presented.Finally,possible future research directions are suggested.展开更多
Blesse D ncube knew there would be lucrative opportunities in learning Chinese.Therefore,the Zimbabwean chose to major in Chinese while doing her Bachelor of Arts and when she graduated in 2010,the 28-year-old found a...Blesse D ncube knew there would be lucrative opportunities in learning Chinese.Therefore,the Zimbabwean chose to major in Chinese while doing her Bachelor of Arts and when she graduated in 2010,the 28-year-old found a wealth of steady work in translation coming her way.展开更多
Machine translation(MT)is a technique that leverages computers to translate human languages automatically.Nowadays,neural machine translation(NMT)which models direct mapping between source and target languages with de...Machine translation(MT)is a technique that leverages computers to translate human languages automatically.Nowadays,neural machine translation(NMT)which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT.This article makes a review of NMT framework,discusses the challenges in NMT,introduces some exciting recent progresses and finally looks forward to some potential future research trends.展开更多
基金supported by Natural Science Foundation of China(Nos.62006224 and 62122088).
文摘Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another.Recently,Transformer-based neural machine translation(NMT)has achieved great break-throughs and has become a new mainstream method in both methodology and applications.In this article,we conduct an overview of Transformer-based NMT and its extension to other tasks.Specifically,we first introduce the framework of Transformer,discuss the main challenges in NMT and list the representative methods for each challenge.Then,the public resources and toolkits in NMT are listed.Meanwhile,the extensions of Transformer in other tasks,including the other natural language processing tasks,computer vision tasks,audio tasks and multi-modal tasks,are briefly presented.Finally,possible future research directions are suggested.
文摘Blesse D ncube knew there would be lucrative opportunities in learning Chinese.Therefore,the Zimbabwean chose to major in Chinese while doing her Bachelor of Arts and when she graduated in 2010,the 28-year-old found a wealth of steady work in translation coming her way.
基金the National Natural Science Foundation of China(Grant Nos.U1836221 and 61673380)the Beijing Municipal Science and Technology Project(Grant No.Z181100008918017)。
文摘Machine translation(MT)is a technique that leverages computers to translate human languages automatically.Nowadays,neural machine translation(NMT)which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT.This article makes a review of NMT framework,discusses the challenges in NMT,introduces some exciting recent progresses and finally looks forward to some potential future research trends.
